Circuit Synthesis Options

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class haiqu.sdk.AnalyticalSynthesisOptions(*, fine_tuning_iterations=0, method=‘analytical’, num_layers=2, layer_type=‘V’, bond_dim_layer=2, entanglement_cutoff=1e-06)

• Parameters:
  • num_layers (int) — Number of disentangling layers. More layers raise the fidelity at the cost of a deeper circuit. Defaults to 2.
  • layer_type (Literal [ ‘linear’ , ‘reverse_linear’ , ‘V’ ]) — Geometry of the disentangler staircase, one of "V", "linear" and "reverse_linear". Defaults to "V".
  • bond_dim_layer (int | None) — Bond dimension each layer is truncated to, which sets the width of that layer’s gates. None sets it to the embedding’s feature density, and to 2 for a density below that, so that a layer is always wide enough to absorb one feature. It applies to the density in use, whether it was requested directly or derived from num_qubits, and only entangled_manifold_embedding() accepts it. Defaults to 2.
  • entanglement_cutoff (float) — Bonds carrying less entanglement than this threshold are left without a gate. Applied from the second layer onward. Defaults to 1e-6.
  • fine_tuning_iterations (int)
  • method (Literal [ ‘analytical’ ])

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Example

>>> from haiqu.sdk import AnalyticalSynthesisOptions
>>> AnalyticalSynthesisOptions(num_layers=3, entanglement_cutoff=1e-8)
>>> # the equivalent dict, which every data loading method also accepts
>>> {"method": "analytical", "num_layers": 3, "entanglement_cutoff": 1e-8}

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class haiqu.sdk.BlockwiseSynthesisOptions(*, fine_tuning_iterations=0, method=‘blockwise’, num_layers=2, layer_type=‘V’, bond_dim_layer=2)

• Parameters:
  • num_layers (int) — Number of disentangling layers. More layers raise the fidelity at the cost of a deeper circuit. Defaults to 2.
  • layer_type (Literal [ ‘linear’ , ‘reverse_linear’ , ‘V’ ]) — Geometry of the disentangler staircase, one of "V", "linear" and "reverse_linear". Defaults to "V".
  • bond_dim_layer (int | None) — Bond dimension each layer is truncated to, which sets the width of that layer’s gates. None sets it to the embedding’s feature density, and to 2 for a density below that, so that a layer is always wide enough to absorb one feature. It applies to the density in use, whether it was requested directly or derived from num_qubits, and only entangled_manifold_embedding() accepts it. Defaults to 2.
  • fine_tuning_iterations (int)
  • method (Literal [ ‘blockwise’ ])

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Example

>>> from haiqu.sdk import BlockwiseSynthesisOptions
>>> BlockwiseSynthesisOptions(num_layers=2, bond_dim_layer=4)
>>> # the equivalent dict, which every data loading method also accepts
>>> {"method": "blockwise", "num_layers": 2, "bond_dim_layer": 4}

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class haiqu.sdk.VariationalSynthesisOptions(*, fine_tuning_iterations=0, method=‘variational’, num_layers=2, layer_type=‘brickwork’, gate_cnots=2, force_complex=False, entanglement_cutoff=1e-06)

• Parameters:
  • num_layers (int) — Number of disentangling layers. More layers raise the fidelity at the cost of a deeper circuit. Defaults to 2.
  • layer_type (Literal [ ‘linear’ , ‘reverse_linear’ , ‘V’ , ‘brickwork’ ]) — Geometry of the gate layers, one of "brickwork", "V", "linear" and "reverse_linear". Defaults to "brickwork".
  • gate_cnots (Literal *[*1 , 2 , 3 ]) — Number of CX gates in each two-qubit gate. More CX gates make a gate able to represent more entanglement. One of 1, 2 or 3. Defaults to 2.
  • force_complex (bool) — Use complex rotations even when the target state is real. A real target is preparable with real rotations alone, which halves the parameter count if not forced. Defaults to False.
  • entanglement_cutoff (float) — Bonds carrying less entanglement than this threshold are left without a gate. Defaults to 1e-6.
  • fine_tuning_iterations (int)
  • method (Literal [ ‘variational’ ])

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Example

>>> from haiqu.sdk import VariationalSynthesisOptions
>>> VariationalSynthesisOptions(num_layers=4, gate_cnots=3)
>>> # the equivalent dict, which every data loading method also accepts
>>> {"method": "variational", "num_layers": 4, "gate_cnots": 3}

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class haiqu.sdk.DataLoadingSynthesisOptions(*, fine_tuning_iterations=0)

• Parameters: fine_tuning_iterations (int) — Maximum refinement iterations that are run after the circuit is built to improve the produced state. 0 skips the refinement. Defaults to 0.